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Understanding the Limits of Deep Tabular Methods with Temporal Shift
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Shows that temporal splitting choices (training lag, validation bias) hurt deep tabular models under temporal shift, proposes an improved temporal split protocol, and adds a Fourier-based temporal embedding of timestamps as a plug-and-play module.
Problem
Deep tabular models degrade under temporal distribution shifts; standard temporal splits for model selection introduce training lag and validation bias, and learned representations lose periodic and trend information.
Approach
A training protocol that minimizes training lag and validation bias (validation set placed symmetric to test-time shift, with zero lag), plus a learnable Fourier-series timestamp embedding (periodic terms with pre-defined periods, ReLU-linear projection, and optional z-scored trend) that is fed to the model to retain temporal information.
Key results
- The proposed zero-lag temporal split gives +2.18% average improvement over the original temporal split, comparable to random split (+2.17%) with much lower instability (std increase 16.69% vs 153.81%).
- Temporal embedding gives small average gains (+0.54% for MLP, MLP-PLR, ModernNCA in the table, +1.31% for MLP) and the direct-to-backbone variant reaches +0.41% for ModernNCA and +0.26% for MLP-PLR.
- Random split improves performance for most methods but is unstable across splits and seeds, with some standard deviations increasing by hundreds of percent.
Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Replace the original temporal split with a split that minimizes training lag and aligns validation shift with test shift | TabReD tabular methods · TabReD | average performance improvement: 2.18\% vs. 2.17\% on average | Helped |
| Use random splitting instead of the original temporal split | TabReD tabular methods · TabReD | average performance improvement: $+$2.17\% | Helped |
| Reduce the training lag by using split (b) instead of split (a) | MLP-PLR, ModernNCA, and TabM · TabReD | average performance improvement: 1.62\% total average improvement; ModernNCA: 2.19\% | Helped |
| Reduce validation bias by aligning the validation shift more closely with the test shift | MLP-PLR, ModernNCA, and TabM · TabReD | average performance improvement: 0.59\% total average improvement; TabM: 0.83\% | Helped |
| Use opposite-direction validation split (d) rather than split (b) | TabReD tabular methods · TabReD | performance: 0.91\% | Hurt |
| Add the proposed Fourier-based temporal embedding | MLP, MLP-PLR, and ModernNCA · TabReD | average performance improvement: MLP: $+$1.31\%; MLP-PLR: $+$0.01\%; ModernNCA: $+$0.30\% | Helped |
| Use a non-learnable numeric timestamp feature or decomposed calendar features instead of the proposed temporal embedding (ablation) | TabReD tabular methods · TabReD | average performance improvement: Num: $-$0.05\%; Time: $-$0.39\% | Hurt |
| Feed temporal embedding directly to the model backbone (ablation) | MLP-PLR, TabM, and ModernNCA · TabReD | average performance improvement: MLP-PLR: $+$0.01\% -> $+$0.26\%; TabM: $+$0.07\% -> $+$0.20\%; ModernNCA: $+$0.30\% -> $+$0.41\% | Helped |
| Use PLR timestamp embedding rather than the proposed Fourier-based temporal embedding (ablation) | TabReD tabular methods · TabReD | average performance improvement: PLR: $+$0.25\%; proposed embedding: $+$0.54\% | Mixed |
Surprising findings
- A simple random split of training data outperforms the standard temporal split on TabReD, suggesting validation strategy is a major hidden factor in temporal shift evaluation.
- Non-learnable timestamp embeddings (e.g., treating timestamps as numerical features) often degrade performance, consistent with timestamps being treated as noise.
Limitations
- Gains from the temporal embedding are small and model-dependent; MLP-PLR and TabM show limited gains due to incompatibility with PLR embedding.
- The proposed splitting protocol's equivalence assumption fails for datasets with non-uniform temporal sampling (e.g., SH, EO), and random split remains unstable.
- Periodicity priors (yearly, monthly, weekly, daily) are pre-defined by hand per dataset.
Experimental setup
Datasets: TabReD (HI, EO, HD, SH, CT, DE, MR, WE)
Benchmarks: TabReD
Metrics: AUC RMSE robust average percentage improvement standard deviation robustness score
Baselines: MLP MLP-PLR FT-Transformer SNN DCNv2 TabR ModernNCA TabM XGBoost CatBoost LightGBM RandomForest
Builds on: PLR embedding ModernNCA TabM TabReD
Closest papers by meaning
- Feature-aware Modulation for Learning from Temporal Tabular Data (NeurIPS 2025 · similarity 0.88)
- TabDPT: Scaling Tabular Foundation Models on Real Data (NeurIPS 2025 · similarity 0.85)
- Fully Test-time Adaptation for Tabular Data (AAAI 2025 · similarity 0.85)
- TabFSBench: Tabular Benchmark for Feature Shifts in Open Environments (ICML 2025 · similarity 0.85)
- Drift-Resilient TabPFN: In-Context Learning Temporal Distribution Shifts on Tabular Data (NeurIPS 2024 · similarity 0.84)
- Tabular Foundation Model for Generative Modelling (NeurIPS 2026 · similarity 0.84)
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